##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Network health predictions and optimization recommendation using Deep learning Neural network models and Reinforcement learning
Time series prediction of network parameters and detecting network health with network performance optimization, has been an interesting problem to solve for researchers in the field of Machine Learning and Data Mining community. These use cases are present across different industries like retail, telecom, transport with good presence in Telecom industry. However, there remains a challenge in getting a good prediction accuracy and efficiency while solving these problems. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, and traditional ML models are applied on top of these transformations to get decent accuracy. These methods are mostly ad-hoc and the performance of these models are limited as there is a separate process to extract features and another process to predict. Recommending an optimal parameters for network is normally done by training more data in traditional supervised models. There lies a challenge in the supervised learning models as these models are data hungry. If the data is insufficient, the traditional supervised models fail to converge, and mining patterns in the data can be a challenge. To address the first challenge, we propose end-to-end neural network architecture models such as Univariate/Multivariate-LSTM, LSTM - Convolutional Neural Network , CNN, LSTM-CNN which incorporates feature extraction and prediction in a single framework. To address the second challenge, Deep Reinforcement learning has been used to recommend the optimal parameters with predicted network parameters which in turn can lead to good network health. We did comprehensive empirical evaluation with various proposed methods on a large number of benchmark datasets, the approach based on Deep learning neural network models and Deep reinforcement learning methods in network parameter optimization has provided a good accuracy when compared to the existing models.
BENEFITS OF OUR PROPOSED SOLUTION
CURRENT SOLUTION SCENARIO
PROPOSED SOLUTION SCENARIO
DIFFERENT MODELS /APPROACHES AND RESULTS
CNN LSTM ARCHITECTURE
COMPLETE PROPOSED SOLUTION ARCHITECTURE
TRADE-OFFS IN PROCESS FOR SOLUTION
PRIVACY, REGULATORY AND ETHICAL CONSIDERATIONS FOR THE DESIGNED SOLUTION
ADDITIONAL DETAILS THAT WILL BE SHARED DURING PRESENTATION
Anaconda,python installation to be completed in laptop
I am a Senior Data Scientist from Ericsson GAIA (R&D). I have 18+ Years of experience in the areas of Telecom, Retail services, Sales and transportations, Financial and Banking and delivered projects of various sizes. I have played several roles like Senior data scientist, AI technology Architect Delivery Manager, Analytics Tech lead, Data scientist, Data Statistical Analyst, Solution Project Manager, Technical/Project lead and developer. My key Capabilities are Machine learning, Analytics, Artificial intelligence, Deep learning, Reinforcement learning, Python, IBM Watson, Azure ML, Biometrics (Facial and Emotion recognition), Predictive Modeling, and Speech to Text.